{
 "cells": [
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-24T12:43:19.417189Z",
     "start_time": "2025-02-24T12:43:16.606925Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from sklearn.feature_extraction import DictVectorizer\n",
    "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
    "from sklearn.feature_selection import VarianceThreshold\n",
    "from sklearn.decomposition import PCA\n",
    "import jieba\n",
    "import numpy as np\n",
    "from sklearn.impute import SimpleImputer"
   ],
   "id": "cfc2ecaf3828381d",
   "outputs": [],
   "execution_count": 6
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-24T12:43:21.384417Z",
     "start_time": "2025-02-24T12:43:21.373054Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def dictvec():\n",
    "    \"\"\"\n",
    "    字典数据抽取\n",
    "    :return: None\n",
    "    \"\"\"\n",
    "    # 实例化\n",
    "    # sparse改为True,输出的是每个不为零位置的坐标，稀疏矩阵可以节省存储空间\n",
    "    #矩阵中存在大量的0，sparse存储只记录非零位置，节省空间的作用\n",
    "    #Vectorizer中文含义是矢量器的含义\n",
    "    # dict1 = DictVectorizer(sparse=True)  # 把sparse改为True看看\n",
    "    dict1 = DictVectorizer()  # 默认sparse=False\n",
    "    #每个样本都是一个字典，有三个样本\n",
    "    # 调用fit_transform\n",
    "    data = dict1.fit_transform([{'city': '北京', 'temperature': 100},\n",
    "                               {'city': '上海', 'temperature': 60},\n",
    "                               {'city': '深圳', 'temperature': 30}])\n",
    "    print(data)\n",
    "    print('-' * 50)\n",
    "    # 字典中的一些类别数据，分别进行转换成特征\n",
    "    print(dict1.get_feature_names_out())\n",
    "    print('-' * 50)\n",
    "    print(dict1.inverse_transform(data))  #去看每个特征代表的含义，逆转回去\n",
    "\n",
    "    return None\n",
    "\n",
    "\n",
    "dictvec()"
   ],
   "id": "c7b89fb8ca23a027",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<Compressed Sparse Row sparse matrix of dtype 'float64'\n",
      "\twith 6 stored elements and shape (3, 4)>\n",
      "  Coords\tValues\n",
      "  (0, 1)\t1.0\n",
      "  (0, 3)\t100.0\n",
      "  (1, 0)\t1.0\n",
      "  (1, 3)\t60.0\n",
      "  (2, 2)\t1.0\n",
      "  (2, 3)\t30.0\n",
      "--------------------------------------------------\n",
      "['city=上海' 'city=北京' 'city=深圳' 'temperature']\n",
      "--------------------------------------------------\n",
      "[{'city=北京': np.float64(1.0), 'temperature': np.float64(100.0)}, {'city=上海': np.float64(1.0), 'temperature': np.float64(60.0)}, {'city=深圳': np.float64(1.0), 'temperature': np.float64(30.0)}]\n"
     ]
    }
   ],
   "execution_count": 7
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-24T12:43:24.389564Z",
     "start_time": "2025-02-24T12:43:24.375249Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def couvec():\n",
    "    # 实例化CountVectorizer\n",
    "    # max_df, min_df整数：指每个词的所有文档词频数不小于最小值，出现该词的文档数目小于等于max_df\n",
    "    # max_df, min_df小数(0-1之间的）：某个词的出现的次数／所有文档数量\n",
    "    # min_df=2\n",
    "    # 默认会去除单个字母的单词，默认认为这个词对整个样本没有影响,认为其没有语义\n",
    "    vector = CountVectorizer(min_df=2)\n",
    "\n",
    "    # 调用fit_transform输入并转换数据\n",
    "\n",
    "    res = vector.fit_transform(\n",
    "        [\"life is  short,i like python life\",\n",
    "         \"life is too long,i dislike python\",\n",
    "         \"life is short\"])\n",
    "\n",
    "    # 打印结果,把每个词都分离了\n",
    "    print(vector.get_feature_names_out())\n",
    "    print('-'*50)\n",
    "    print(res)\n",
    "    print('-'*50)\n",
    "    print(type(res))\n",
    "    # 对照feature_names，标记每个词出现的次数\n",
    "    print('-'*50)\n",
    "    print(res.toarray()) #稀疏矩阵转换为数组\n",
    "    print('-'*50)\n",
    "    #拿每个样本里的特征进行显示\n",
    "    print(vector.inverse_transform(res))\n",
    "\n",
    "\n",
    "couvec()"
   ],
   "id": "b73fda6989d958a",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['is' 'life' 'python' 'short']\n",
      "--------------------------------------------------\n",
      "<Compressed Sparse Row sparse matrix of dtype 'int64'\n",
      "\twith 10 stored elements and shape (3, 4)>\n",
      "  Coords\tValues\n",
      "  (0, 1)\t2\n",
      "  (0, 0)\t1\n",
      "  (0, 3)\t1\n",
      "  (0, 2)\t1\n",
      "  (1, 1)\t1\n",
      "  (1, 0)\t1\n",
      "  (1, 2)\t1\n",
      "  (2, 1)\t1\n",
      "  (2, 0)\t1\n",
      "  (2, 3)\t1\n",
      "--------------------------------------------------\n",
      "<class 'scipy.sparse._csr.csr_matrix'>\n",
      "--------------------------------------------------\n",
      "[[1 2 1 1]\n",
      " [1 1 1 0]\n",
      " [1 1 0 1]]\n",
      "--------------------------------------------------\n",
      "[array(['life', 'is', 'short', 'python'], dtype='<U6'), array(['life', 'is', 'python'], dtype='<U6'), array(['life', 'is', 'short'], dtype='<U6')]\n"
     ]
    }
   ],
   "execution_count": 8
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-24T12:43:26.494259Z",
     "start_time": "2025-02-24T12:43:26.487942Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def countvec():\n",
    "    \"\"\"\n",
    "    对文本进行特征值化,单个汉字单个字母不统计，因为单个汉字字母没有意义\n",
    "    :return: None\n",
    "    \"\"\"\n",
    "    cv = CountVectorizer()\n",
    "\n",
    "    data = cv.fit_transform([\"人生苦短，我 喜欢 python python\", \"人生漫长，不用 python\"])\n",
    "\n",
    "    print(cv.get_feature_names_out())\n",
    "    print('-'*50)\n",
    "    print(data) #稀疏存储，只记录非零位置\n",
    "    print('-'*50)\n",
    "    print(data.toarray())\n",
    "\n",
    "    return None\n",
    "\n",
    "\n",
    "countvec()"
   ],
   "id": "5b040988ce353f82",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['python' '不用' '人生漫长' '人生苦短' '喜欢']\n",
      "--------------------------------------------------\n",
      "<Compressed Sparse Row sparse matrix of dtype 'int64'\n",
      "\twith 6 stored elements and shape (2, 5)>\n",
      "  Coords\tValues\n",
      "  (0, 3)\t1\n",
      "  (0, 4)\t1\n",
      "  (0, 0)\t2\n",
      "  (1, 0)\t1\n",
      "  (1, 2)\t1\n",
      "  (1, 1)\t1\n",
      "--------------------------------------------------\n",
      "[[2 0 0 1 1]\n",
      " [1 1 1 0 0]]\n"
     ]
    }
   ],
   "execution_count": 9
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-24T12:43:34.992514Z",
     "start_time": "2025-02-24T12:43:34.349228Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def cutword():\n",
    "    \"\"\"\n",
    "    通过jieba对中文进行分词\n",
    "    :return:\n",
    "    \"\"\"\n",
    "    con1 = jieba.cut(\"今天很残酷，明天更残酷，后天很美好，但绝对大部分是死在明天晚上，所以每个人不要放弃今天。\")\n",
    "\n",
    "    con2 = jieba.cut(\"我们看到的从很远星系来的光是在几百万年之前发出的，这样当我们看到宇宙时，我们是在看它的过去。\")\n",
    "\n",
    "    con3 = jieba.cut(\"如果只用一种方式了解某样事物，你就不会真正了解它。了解事物真正含义的秘密取决于如何将其与我们所了解的事物相联系。\")\n",
    "\n",
    "    # 转换成列表\n",
    "    print(type(con1))\n",
    "    print('-' * 50)\n",
    "    # 把生成器转换成列表\n",
    "    content1 = list(con1)\n",
    "    content2 = list(con2)\n",
    "    content3 = list(con3)\n",
    "    print(content1)\n",
    "    print(content2)\n",
    "    print(content3)\n",
    "    # 把列表转换成字符串,每个词之间用空格隔开\n",
    "    print('-' * 50)\n",
    "    c1 = ' '.join(content1)\n",
    "    c2 = ' '.join(content2)\n",
    "    c3 = ' '.join(content3)\n",
    "\n",
    "    return c1, c2, c3\n",
    "\n",
    "\n",
    "def hanzivec():\n",
    "    \"\"\"\n",
    "    中文特征值化\n",
    "    :return: None\n",
    "    \"\"\"\n",
    "    c1, c2, c3 = cutword() #jieba分词好的中文文本\n",
    "    print('-'*50)\n",
    "    print(c1)\n",
    "    print(c2)\n",
    "    print(c3)\n",
    "    print('-'*50)\n",
    "\n",
    "    cv = CountVectorizer()\n",
    "\n",
    "    data = cv.fit_transform([c1, c2, c3])\n",
    "\n",
    "    print(cv.get_feature_names_out())\n",
    "\n",
    "    print(data.toarray())\n",
    "\n",
    "    return None\n",
    "\n",
    "# cutword()\n",
    "hanzivec()"
   ],
   "id": "ae36b9cb5100a25e",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Building prefix dict from the default dictionary ...\n",
      "Loading model from cache C:\\Users\\Lenovo\\AppData\\Local\\Temp\\jieba.cache\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'generator'>\n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading model cost 0.631 seconds.\n",
      "Prefix dict has been built successfully.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['今天', '很', '残酷', '，', '明天', '更', '残酷', '，', '后天', '很', '美好', '，', '但', '绝对', '大部分', '是', '死', '在', '明天', '晚上', '，', '所以', '每个', '人', '不要', '放弃', '今天', '。']\n",
      "['我们', '看到', '的', '从', '很', '远', '星系', '来', '的', '光是在', '几百万年', '之前', '发出', '的', '，', '这样', '当', '我们', '看到', '宇宙', '时', '，', '我们', '是', '在', '看', '它', '的', '过去', '。']\n",
      "['如果', '只用', '一种', '方式', '了解', '某样', '事物', '，', '你', '就', '不会', '真正', '了解', '它', '。', '了解', '事物', '真正', '含义', '的', '秘密', '取决于', '如何', '将', '其', '与', '我们', '所', '了解', '的', '事物', '相', '联系', '。']\n",
      "--------------------------------------------------\n",
      "--------------------------------------------------\n",
      "今天 很 残酷 ， 明天 更 残酷 ， 后天 很 美好 ， 但 绝对 大部分 是 死 在 明天 晚上 ， 所以 每个 人 不要 放弃 今天 。\n",
      "我们 看到 的 从 很 远 星系 来 的 光是在 几百万年 之前 发出 的 ， 这样 当 我们 看到 宇宙 时 ， 我们 是 在 看 它 的 过去 。\n",
      "如果 只用 一种 方式 了解 某样 事物 ， 你 就 不会 真正 了解 它 。 了解 事物 真正 含义 的 秘密 取决于 如何 将 其 与 我们 所 了解 的 事物 相 联系 。\n",
      "--------------------------------------------------\n",
      "['一种' '不会' '不要' '之前' '了解' '事物' '今天' '光是在' '几百万年' '发出' '取决于' '只用' '后天' '含义'\n",
      " '大部分' '如何' '如果' '宇宙' '我们' '所以' '放弃' '方式' '明天' '星系' '晚上' '某样' '残酷' '每个'\n",
      " '看到' '真正' '秘密' '绝对' '美好' '联系' '过去' '这样']\n",
      "[[0 0 1 0 0 0 2 0 0 0 0 0 1 0 1 0 0 0 0 1 1 0 2 0 1 0 2 1 0 0 0 1 1 0 0 0]\n",
      " [0 0 0 1 0 0 0 1 1 1 0 0 0 0 0 0 0 1 3 0 0 0 0 1 0 0 0 0 2 0 0 0 0 0 1 1]\n",
      " [1 1 0 0 4 3 0 0 0 0 1 1 0 1 0 1 1 0 1 0 0 1 0 0 0 1 0 0 0 2 1 0 0 1 0 0]]\n"
     ]
    }
   ],
   "execution_count": 10
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-24T12:43:37.926405Z",
     "start_time": "2025-02-24T12:43:37.915455Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 规范{'l1'，'l2'}，默认='l2'\n",
    "# 每个输出行都有单位范数，或者：\n",
    "#\n",
    "# 'l2'：向量元素的平方和为 1。当应用 l2 范数时，两个向量之间的余弦相似度是它们的点积。\n",
    "#\n",
    "# 'l1'：向量元素的绝对值之和为 1。参见preprocessing.normalize。\n",
    "\n",
    "# smooth_idf布尔值，默认 = True\n",
    "# 通过在文档频率上加一来平滑 idf 权重，就好像看到一个额外的文档包含集合中的每个术语恰好一次。防止零分裂。\n",
    "# 比如训练集中有某个词，测试集中没有，就是生僻词，就会造成n(x)分母为零，log(n/n(x)),从而出现零分裂\n",
    "\n",
    "def tfidfvec():\n",
    "    \"\"\"\n",
    "    中文特征值化,计算tfidf值\n",
    "    :return: None\n",
    "    \"\"\"\n",
    "    c1, c2, c3 = cutword()\n",
    "\n",
    "    print(c1, c2, c3)\n",
    "    # print(type([c1, c2, c3]))\n",
    "    tf = TfidfVectorizer(smooth_idf=True)\n",
    "\n",
    "    data = tf.fit_transform([c1, c2, c3])\n",
    "\n",
    "    print(tf.get_feature_names_out())\n",
    "    print('-'*50)\n",
    "    print(type(data))\n",
    "    print('-'*50)\n",
    "    print(data.toarray())\n",
    "\n",
    "    return None\n",
    "\n",
    "\n",
    "tfidfvec()"
   ],
   "id": "f693c3608d173553",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'generator'>\n",
      "--------------------------------------------------\n",
      "['今天', '很', '残酷', '，', '明天', '更', '残酷', '，', '后天', '很', '美好', '，', '但', '绝对', '大部分', '是', '死', '在', '明天', '晚上', '，', '所以', '每个', '人', '不要', '放弃', '今天', '。']\n",
      "['我们', '看到', '的', '从', '很', '远', '星系', '来', '的', '光是在', '几百万年', '之前', '发出', '的', '，', '这样', '当', '我们', '看到', '宇宙', '时', '，', '我们', '是', '在', '看', '它', '的', '过去', '。']\n",
      "['如果', '只用', '一种', '方式', '了解', '某样', '事物', '，', '你', '就', '不会', '真正', '了解', '它', '。', '了解', '事物', '真正', '含义', '的', '秘密', '取决于', '如何', '将', '其', '与', '我们', '所', '了解', '的', '事物', '相', '联系', '。']\n",
      "--------------------------------------------------\n",
      "今天 很 残酷 ， 明天 更 残酷 ， 后天 很 美好 ， 但 绝对 大部分 是 死 在 明天 晚上 ， 所以 每个 人 不要 放弃 今天 。 我们 看到 的 从 很 远 星系 来 的 光是在 几百万年 之前 发出 的 ， 这样 当 我们 看到 宇宙 时 ， 我们 是 在 看 它 的 过去 。 如果 只用 一种 方式 了解 某样 事物 ， 你 就 不会 真正 了解 它 。 了解 事物 真正 含义 的 秘密 取决于 如何 将 其 与 我们 所 了解 的 事物 相 联系 。\n",
      "['一种' '不会' '不要' '之前' '了解' '事物' '今天' '光是在' '几百万年' '发出' '取决于' '只用' '后天' '含义'\n",
      " '大部分' '如何' '如果' '宇宙' '我们' '所以' '放弃' '方式' '明天' '星系' '晚上' '某样' '残酷' '每个'\n",
      " '看到' '真正' '秘密' '绝对' '美好' '联系' '过去' '这样']\n",
      "--------------------------------------------------\n",
      "<class 'scipy.sparse._csr.csr_matrix'>\n",
      "--------------------------------------------------\n",
      "[[0.         0.         0.21821789 0.         0.         0.\n",
      "  0.43643578 0.         0.         0.         0.         0.\n",
      "  0.21821789 0.         0.21821789 0.         0.         0.\n",
      "  0.         0.21821789 0.21821789 0.         0.43643578 0.\n",
      "  0.21821789 0.         0.43643578 0.21821789 0.         0.\n",
      "  0.         0.21821789 0.21821789 0.         0.         0.        ]\n",
      " [0.         0.         0.         0.2410822  0.         0.\n",
      "  0.         0.2410822  0.2410822  0.2410822  0.         0.\n",
      "  0.         0.         0.         0.         0.         0.2410822\n",
      "  0.55004769 0.         0.         0.         0.         0.2410822\n",
      "  0.         0.         0.         0.         0.48216441 0.\n",
      "  0.         0.         0.         0.         0.2410822  0.2410822 ]\n",
      " [0.15698297 0.15698297 0.         0.         0.62793188 0.47094891\n",
      "  0.         0.         0.         0.         0.15698297 0.15698297\n",
      "  0.         0.15698297 0.         0.15698297 0.15698297 0.\n",
      "  0.1193896  0.         0.         0.15698297 0.         0.\n",
      "  0.         0.15698297 0.         0.         0.         0.31396594\n",
      "  0.15698297 0.         0.         0.15698297 0.         0.        ]]\n"
     ]
    }
   ],
   "execution_count": 11
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-24T12:43:40.435101Z",
     "start_time": "2025-02-24T12:43:40.427997Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def mm():\n",
    "    \"\"\"\n",
    "    归一化处理\n",
    "    :return: NOne\n",
    "    \"\"\"\n",
    "    # 归一化缺点 容易受极值的影响\n",
    "    #feature_range代表特征值范围，一般设置为(0,1),或者(-1,1),默认是(0,1)\n",
    "    mm = MinMaxScaler(feature_range=(0, 1))\n",
    "\n",
    "    data = mm.fit_transform([[90, 2, 10, 40], [60, 4, 15, 45], [75, 3, 13, 46]])\n",
    "\n",
    "    print(data)\n",
    "    print('-'*50)\n",
    "    out=mm.transform([[1, 2, 3, 4],[6, 5, 8, 7]])\n",
    "    print(out)\n",
    "    return None\n",
    "    #transform和fit_transform不同是，transform用于测试集，而且不会重新找最小值和最大值\n",
    "\n",
    "\n",
    "mm()"
   ],
   "id": "8fbad177cf1082dc",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.         0.         0.         0.        ]\n",
      " [0.         1.         1.         0.83333333]\n",
      " [0.5        0.5        0.6        1.        ]]\n",
      "--------------------------------------------------\n",
      "[[-1.96666667  0.         -1.4        -6.        ]\n",
      " [-1.8         1.5        -0.4        -5.5       ]]\n"
     ]
    }
   ],
   "execution_count": 12
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-24T12:43:42.984016Z",
     "start_time": "2025-02-24T12:43:42.977491Z"
    }
   },
   "cell_type": "code",
   "source": "(1-60)/30",
   "id": "ec24dbb64bb83e96",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.9666666666666666"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 13
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-24T12:43:44.405295Z",
     "start_time": "2025-02-24T12:43:44.398328Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def stand():\n",
    "    \"\"\"\n",
    "    标准化缩放，不是标准正太分布，只均值为0，方差为1的分布\n",
    "    :return:\n",
    "    \"\"\"\n",
    "    std = StandardScaler()\n",
    "\n",
    "    data = std.fit_transform([[1., -1., 3.], [2., 4., 2.], [4., 6., -1.]])\n",
    "\n",
    "    print(data)\n",
    "    print('-' * 50)\n",
    "    print(std.mean_)\n",
    "    print('-' * 50)\n",
    "    print(std.var_)\n",
    "    print(std.n_samples_seen_)  # 样本数\n",
    "    return data\n",
    "\n",
    "\n",
    "data=stand()"
   ],
   "id": "6602f55b50757035",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-1.06904497 -1.35873244  0.98058068]\n",
      " [-0.26726124  0.33968311  0.39223227]\n",
      " [ 1.33630621  1.01904933 -1.37281295]]\n",
      "--------------------------------------------------\n",
      "[2.33333333 3.         1.33333333]\n",
      "--------------------------------------------------\n",
      "[1.55555556 8.66666667 2.88888889]\n",
      "3\n"
     ]
    }
   ],
   "execution_count": 14
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-24T12:43:46.943125Z",
     "start_time": "2025-02-24T12:43:46.937793Z"
    }
   },
   "cell_type": "code",
   "source": "type(data)",
   "id": "44444e2fc4723a2",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 15
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-24T12:43:53.933527Z",
     "start_time": "2025-02-24T12:43:53.927501Z"
    }
   },
   "cell_type": "code",
   "source": [
    "std1 = StandardScaler()\n",
    "#为了证明上面输出的结果的均值是为0的，方差为1\n",
    "data1 = std1.fit_transform(data)\n",
    "# print(data1)  #这个并不是我们想看的，没意义\n",
    "# 均值\n",
    "print(std1.mean_)\n",
    "# 方差\n",
    "print(std1.var_)"
   ],
   "id": "274c26c9832a57ba",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-1.48029737e-16  7.40148683e-17  7.40148683e-17]\n",
      "[1. 1. 1.]\n"
     ]
    }
   ],
   "execution_count": 16
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-24T12:44:08.372365Z",
     "start_time": "2025-02-24T12:44:08.367334Z"
    }
   },
   "cell_type": "code",
   "source": "(np.square(1-2.333)+np.square(2-2.333)+np.square(4-2.333))/3",
   "id": "d1a1c10ccc115669",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(1.5555556666666668)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 17
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-24T12:44:20.469812Z",
     "start_time": "2025-02-24T12:44:20.464671Z"
    }
   },
   "cell_type": "code",
   "source": "(1-2.333)/np.sqrt(1.55555)",
   "id": "552567fde0cd50e2",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(-1.068779614944516)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 18
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-24T12:44:43.233973Z",
     "start_time": "2025-02-24T12:44:43.227Z"
    }
   },
   "cell_type": "code",
   "source": [
    "#下面是填补，针对删除，可以用pd和np\n",
    "def im():\n",
    "    \"\"\"\n",
    "    缺失值处理\n",
    "    :return:NOne\n",
    "    \"\"\"\n",
    "    # NaN, nan,缺失值必须是这种形式，如果是？号(或者其他符号)，就要replace换成这种\n",
    "    im = SimpleImputer(missing_values=np.nan, strategy='mean')\n",
    "\n",
    "    data = im.fit_transform([[1, 2], [np.nan, 3], [7, 6], [3, 2]])\n",
    "\n",
    "    print(data)\n",
    "\n",
    "    return None\n",
    "\n",
    "\n",
    "im()"
   ],
   "id": "964cd329f5a7d78d",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.         2.        ]\n",
      " [3.66666667 3.        ]\n",
      " [7.         6.        ]\n",
      " [3.         2.        ]]\n"
     ]
    }
   ],
   "execution_count": 19
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-24T12:45:13.726045Z",
     "start_time": "2025-02-24T12:45:13.721343Z"
    }
   },
   "cell_type": "code",
   "source": "11/3",
   "id": "26eb63d65c0ccd6a",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.6666666666666665"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 20
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-24T12:45:18.150972Z",
     "start_time": "2025-02-24T12:45:18.145136Z"
    }
   },
   "cell_type": "code",
   "source": "np.nan+10",
   "id": "ec717680192561fd",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "nan"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 21
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-24T12:45:28.318931Z",
     "start_time": "2025-02-24T12:45:28.312747Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def var():\n",
    "    \"\"\"\n",
    "    特征选择-删除低方差的特征\n",
    "    :return: None\n",
    "    \"\"\"\n",
    "    #默认只删除方差为0,threshold是方差阈值，删除比这个值小的那些特征\n",
    "    var = VarianceThreshold(threshold=0.1)\n",
    "\n",
    "    data = var.fit_transform([[0, 2, 0, 3],\n",
    "                              [0, 1, 4, 3],\n",
    "                              [0, 1, 1, 3]])\n",
    "\n",
    "    print(data)\n",
    "    # 获得剩余的特征的列编号\n",
    "    print('The surport is %s' % var.get_support(True))\n",
    "    return None\n",
    "\n",
    "\n",
    "var()\n"
   ],
   "id": "49f7916281b2daca",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[2 0]\n",
      " [1 4]\n",
      " [1 1]]\n",
      "The surport is [1 2]\n"
     ]
    }
   ],
   "execution_count": 22
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-24T12:46:03.738111Z",
     "start_time": "2025-02-24T12:46:03.720665Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def pca():\n",
    "    \"\"\"\n",
    "    主成分分析进行特征降维\n",
    "    :return: None\n",
    "    \"\"\"\n",
    "    # n_ components:小数 0~1 90% 业界选择 90~95%\n",
    "\n",
    "    # 当n_components的值为0到1之间的浮点数时，表示我们希望保留的主成分解释的方差比例。方差比例是指 得到输出的每一列的方差值和除以原有数据方差之和。\n",
    "    # 具体而言，n_components=0.9表示我们希望选择足够的主成分，以使它们解释数据方差的90%。\n",
    "\n",
    "    # n_components如果是整数   减少到的特征数量\n",
    "    # 原始数据方差\n",
    "    original_value = np.array([[2, 8, 4, 5], [6, 3, 0, 8], [5, 4, 9, 1]])\n",
    "    print(np.var(original_value, axis=0).sum()) #最初数据的方差\n",
    "    print('-'* 50)\n",
    "    pca = PCA(n_components=0.9)\n",
    "\n",
    "    data = pca.fit_transform(original_value)\n",
    "\n",
    "    print(data)\n",
    "    print(type(data))\n",
    "    #计算data的方差\n",
    "    print(np.var(data, axis=0).sum())\n",
    "    print('-'*50)\n",
    "    print(pca.explained_variance_ratio_)\n",
    "    # 计算data的方差占总方差的比例\n",
    "    print(pca.explained_variance_ratio_.sum())\n",
    "\n",
    "    return None\n",
    "\n",
    "\n",
    "pca()"
   ],
   "id": "a5b5e5bd91824284",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29.333333333333336\n",
      "--------------------------------------------------\n",
      "[[-1.28620952e-15  3.82970843e+00]\n",
      " [-5.74456265e+00 -1.91485422e+00]\n",
      " [ 5.74456265e+00 -1.91485422e+00]]\n",
      "<class 'numpy.ndarray'>\n",
      "29.333333333333332\n",
      "--------------------------------------------------\n",
      "[0.75 0.25]\n",
      "1.0\n"
     ]
    }
   ],
   "execution_count": 23
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-24T12:54:47.374724Z",
     "start_time": "2025-02-24T12:54:47.370075Z"
    }
   },
   "cell_type": "code",
   "source": "22/29.333333333333336",
   "id": "f0c726981b6e2453",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7499999999999999"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 24
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-24T12:54:56.696372Z",
     "start_time": "2025-02-24T12:54:55.815433Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from matplotlib import pyplot as plt\n",
    "x = np.random.rand(10000) #每个的概率\n",
    "t = np.arange(len(x))\n",
    "plt.plot(t,x,'g.',label=\"Uniform Distribution\")\n",
    "plt.legend(loc=\"upper left\")\n",
    "plt.grid()\n",
    "plt.show()"
   ],
   "id": "5e3af767a6f1faf2",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 25
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "",
   "id": "c2cd86ebccf127cd"
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
